This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal’s purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results.

A least‐squares particle filter (LSPF) is proposed, where the latest measurement is effectively integrated into each sampled particle with the help of the least‐squares estimate, thereby promoting the movement of particles from the prior areas to the high likelihood regions. More importantly, an approach of augmenting the measurement vector is presented to overcome the irreversible problem potentially existing in the proposed filter, thus expanding the application range of the LSPF. The experimental results of bearings‐only tracking demonstrate the better estimation accuracy of the LSPF than the standard particle filter and the auxiliary particle filter.